{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "9TV7IYeqifSv"
      },
      "source": [
        "##### Copyright 2018 The TensorFlow Authors. [Licensed under the Apache License, Version 2.0](#scrollTo=ByZjmtFgB_Y5)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {},
        "colab_type": "code",
        "id": "tRIJp_4m_Afz"
      },
      "outputs": [],
      "source": [
        "// #@title Licensed under the Apache License, Version 2.0 (the \"License\"); { display-mode: \"form\" }\n",
        "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "// you may not use this file except in compliance with the License.\n",
        "// You may obtain a copy of the License at\n",
        "//\n",
        "// https://www.apache.org/licenses/LICENSE-2.0\n",
        "//\n",
        "// Unless required by applicable law or agreed to in writing, software\n",
        "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "// See the License for the specific language governing permissions and\n",
        "// limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "sI1ZtrdiA4aY"
      },
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://www.tensorflow.org/swift/tutorials/python_interoperability\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />TensorFlow.org에서 보기</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/ko/swift/tutorials/python_interoperability.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩(Colab)에서 실행하기</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/ko/swift/tutorials/python_interoperability.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />깃허브(GitHub)에서 소스 보기</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/ko/swift/tutorials/python_interoperability.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "-agGVYp_4GWZ"
      },
      "source": [
        "Note: 이 문서는 텐서플로 커뮤니티에서 번역했습니다. 커뮤니티 번역 활동의 특성상 정확한 번역과 최신 내용을 반영하기 위해 노력함에도\n",
        "불구하고 [공식 영문 문서](https://www.tensorflow.org/?hl=en)의 내용과 일치하지 않을 수 있습니다.\n",
        "이 번역에 개선할 부분이 있다면\n",
        "[tensorflow/docs-l10n](https://github.com/tensorflow/docs-l10n/) 깃헙 저장소로 풀 리퀘스트를 보내주시기 바랍니다.\n",
        "문서 번역이나 리뷰에 참여하려면\n",
        "[docs-ko@tensorflow.org](https://groups.google.com/a/tensorflow.org/forum/#!forum/docs-ko)로\n",
        "메일을 보내주시기 바랍니다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "8sa42_NblqRE"
      },
      "source": [
        "# 파이썬 상호 호환성\n",
        "\n",
        "텐서플로를 위한 스위프트는 파이썬과 상호 호환됩니다.\n",
        "\n",
        "스위프트에서 파이썬 모듈을 임포트해서, 스위프트와 파이썬 사이의 값을 바꾸거나 파이썬 함수를 호출할 수 있습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "colab_type": "code",
        "id": "kZRlD4utdPuX",
        "outputId": "33fa8c6f-48c8-4240-b42c-e9ae0d753b5a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3.6.9 (default, Nov  7 2019, 10:44:02) \r\n",
            "[GCC 8.3.0]\r\n"
          ]
        }
      ],
      "source": [
        "// comment so that Colab does not interpret `#if ...` as a comment\n",
        "#if canImport(PythonKit)\n",
        "    import PythonKit\n",
        "#else\n",
        "    import Python\n",
        "#endif\n",
        "print(Python.version)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "lcsgrcnc490l"
      },
      "source": [
        "## 파이썬 버전 설정"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "JvvMEn2o490m"
      },
      "source": [
        "기본적으로 `import Python`를 하면, 스위프트는 시스템 라이브러리 경로에 따라 설치된 최신 버전의 파이썬을 검색합니다. \n",
        "특정한 파이썬을 설치하려면, `PYTHON_LIBRARY` 환경변수에 설치시 제공받은 `libpython` 공유 라이브러리를 설정합니다.  예를 들어: \n",
        "\n",
        "`export PYTHON_LIBRARY=\"~/anaconda3/lib/libpython3.7m.so\"`\n",
        "\n",
        "정확한 파일명은 파이썬 환경과 플랫폼마다 다를 수 있습니다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "jELTcaDK490n"
      },
      "source": [
        "또는 스위프트가 시스템 라이브러리 경로에서 알맞은 파이썬 버전을 찾도록 해서 `PYTHON_VERSION` 환경변수를 설정할 수 있습니다. `PYTHON_LIBRARY`가 `PYTHON_VERSION` 보다 우선한다는 점을 유의해야 합니다.\n",
        "\n",
        "또한 코드에서 `PYTHON_VERSION` 설정과 동일한 기능을 하는 `PythonLibrary.useVersion` 함수를 호출할 수 있습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "7jEc0ZF8490o"
      },
      "outputs": [],
      "source": [
        "// PythonLibrary.useVersion(2)\n",
        "// PythonLibrary.useVersion(3, 7)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "JEucWkEK490q"
      },
      "source": [
        "__Note: 파이썬을 임포트한 직후, 파이썬 코드를 호출하기 전에 `PythonLibrary.useVersion`을 실행해야 합니다. 이것은 파이썬 버전을 동적으로 바꾸는 데 사용될 수 없습니다.__"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Meb9Mb9P490q"
      },
      "source": [
        "[파이썬 라이브러리 로딩 과정에서 생성되는 디버그 출력](https://github.com/apple/swift/pull/20674#discussion_r235207008)을 확인하기 위해서 `PYTHON_LOADER_LOGGING=1`를 설정하세요.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "rU0WY_sJodio"
      },
      "source": [
        "## 기초\n",
        "\n",
        "스위프트에서 `PythonObject`는 파이썬 객체를 나타냅니다.\n",
        "모든 파이썬 API는 `PythonObject` 인스턴스를 사용하거나 반환합니다.\n",
        "\n",
        "스위프트에서 기본형(숫자 및 배열처럼)은 `PythonObject`로 전환할 수 있습니다. 몇몇 경우에 (`PythonConvertible` 인수를 받는 리터럴과 함수의 경우), 변환이 암묵적으로 일어납니다. 스위프트 값을 `PythonObject`에 명시적으로 지정하려면 `PythonObject` 이니셜라이저를 사용합니다.\n",
        "\n",
        "`PythonObject`는 숫자 연산, 인덱싱, 반복을 포함한 많은 표준 연산을 정의합니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "colab_type": "code",
        "id": "kqXILiXhq-iM",
        "outputId": "5d911661-0364-4457-92c2-6a3865305396"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "4.0\r\n",
            "Hello \r\n",
            "slice(5, 10, None)\r\n",
            "3\r\n",
            "[1, 2, 3]\r\n"
          ]
        }
      ],
      "source": [
        "// 표준 스위프트 자료형을 Python으로 변환합니다.\n",
        "let pythonInt: PythonObject = 1\n",
        "let pythonFloat: PythonObject = 3.0\n",
        "let pythonString: PythonObject = \"Hello Python!\"\n",
        "let pythonRange: PythonObject = PythonObject(5..<10)\n",
        "let pythonArray: PythonObject = [1, 2, 3, 4]\n",
        "let pythonDict: PythonObject = [\"foo\": [0], \"bar\": [1, 2, 3]]\n",
        "\n",
        "// 파이썬 객체에 표준 연산을 수행합니다.\n",
        "print(pythonInt + pythonFloat)\n",
        "print(pythonString[0..<6])\n",
        "print(pythonRange)\n",
        "print(pythonArray[2])\n",
        "print(pythonDict[\"bar\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "colab_type": "code",
        "id": "fEAEyUExXT3I",
        "outputId": "c6468957-96f2-4e89-a088-9590060dca0f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "4.0\r\n",
            "Hello \r\n",
            "5..<10\r\n",
            "3\r\n",
            "[1, 2, 3]\r\n"
          ]
        }
      ],
      "source": [
        "// 파이썬 객체를 다시 스위프트로 변환합니다.\n",
        "let int = Int(pythonInt)!\n",
        "let float = Float(pythonFloat)!\n",
        "let string = String(pythonString)!\n",
        "let range = Range<Int>(pythonRange)!\n",
        "let array: [Int] = Array(pythonArray)!\n",
        "let dict: [String: [Int]] = Dictionary(pythonDict)!\n",
        "\n",
        "// 표준 연산을 수행합니다.\n",
        "// 출력은 파이썬과 동일합니다!\n",
        "print(Float(int) + float)\n",
        "print(string.prefix(6))\n",
        "print(range)\n",
        "print(array[2])\n",
        "print(dict[\"bar\"]!)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "1pMewsl0VgnJ"
      },
      "source": [
        "`PythonObject`는 많은 표준 스위프트 프로토콜에 대해 적합하도록 정의합니다:\n",
        "* `Equatable`\n",
        "* `Comparable`\n",
        "* `Hashable`\n",
        "* `SignedNumeric`\n",
        "* `Strideable`\n",
        "* `MutableCollection`\n",
        "* 모든 `ExpressibleBy_Literal` 프로토콜\n",
        "\n",
        "이러한 적합성은 형안전(type-safe)하지 않다는 점에 유의해야 합니다: 호환되지 않는 `PythonObject` 인스턴스에서 프로토콜 기능을 사용하려고 할 때 충돌이 발생할 수 있습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "colab_type": "code",
        "id": "W9bUsiOxVf_v",
        "outputId": "6aea03d2-4b9f-483f-898f-bfc8768c0f25"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "true\r\n",
            "false\r\n",
            "2\r\n",
            "0 1\r\n",
            "1 2\r\n",
            "2 3\r\n"
          ]
        }
      ],
      "source": [
        "let one: PythonObject = 1\n",
        "print(one == one)\n",
        "print(one < one)\n",
        "print(one + one)\n",
        "\n",
        "let array: PythonObject = [1, 2, 3]\n",
        "for (i, x) in array.enumerated() {\n",
        "  print(i, x)\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "w3lmTRCWT5sS"
      },
      "source": [
        "튜플을 파이썬에서 스위프트로 변환하려면, 정확한 튜플의 길이를 알아야 합니다.\n",
        "\n",
        "다음 인스턴스 메서드 중 하나를 호출합니다:\n",
        "- `PythonObject.tuple2`\n",
        "- `PythonObject.tuple3`\n",
        "- `PythonObject.tuple4`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "colab_type": "code",
        "id": "fQ0HEX89T4mW",
        "outputId": "65f2e142-5cc7-4728-cf2f-1d28dec9d584"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(1, 2, 3) 3\r\n",
            "(1, 2, 3)\r\n"
          ]
        }
      ],
      "source": [
        "let pythonTuple = Python.tuple([1, 2, 3])\n",
        "print(pythonTuple, Python.len(pythonTuple))\n",
        "\n",
        "// 스위프트로 변환합니다.\n",
        "let tuple = pythonTuple.tuple3\n",
        "print(tuple)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Te7sNNx9c_am"
      },
      "source": [
        "## 파이썬 내장 객체\n",
        "\n",
        "전역 `Python` 인터페이스를 활용해 파이썬 내장 객체에 접근합니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "colab_type": "code",
        "id": "jpcOByipc75O",
        "outputId": "8189b995-7cba-4a3f-dc09-f58a08fed85d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'int'>\r\n",
            "3\r\n",
            "6\r\n"
          ]
        }
      ],
      "source": [
        "// `Python.builtins`은 모든 파이썬 내장 객체의 딕셔너리입니다.\n",
        "_ = Python.builtins\n",
        "\n",
        "// 파이썬 내장 객체를 사용합니다.\n",
        "print(Python.type(1))\n",
        "print(Python.len([1, 2, 3]))\n",
        "print(Python.sum([1, 2, 3]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "H2wwUL1tY3JX"
      },
      "source": [
        "## 파이썬 모듈 임포트\n",
        "\n",
        "`Python.import`를 사용하여 파이썬 모듈을 임포트합니다. 이것은 `Python`의 `import` 키워드처럼 동작합니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "colab_type": "code",
        "id": "XrZee8n3Y17_",
        "outputId": "497dd13c-0d11-45ce-f9cf-4014499221ee"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<module 'numpy' from '/usr/local/lib/python3.6/dist-packages/numpy/__init__.py'>\r\n",
            "[[1. 1. 1.]\r\n",
            " [1. 1. 1.]]\r\n"
          ]
        }
      ],
      "source": [
        "let np = Python.import(\"numpy\")\n",
        "print(np)\n",
        "let zeros = np.ones([2, 3])\n",
        "print(zeros)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "hQvza3dUXlr0"
      },
      "source": [
        "안전하게 패키지를 가져오기 위해 예외처리 함수 `Python.attemptImport`를 사용하세요."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "colab_type": "code",
        "id": "QD-uQGuaXhrM",
        "outputId": "42d589a3-baf6-49ef-bb67-c67f8201062e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "nil\r\n"
          ]
        }
      ],
      "source": [
        "let maybeModule = try? Python.attemptImport(\"nonexistent_module\")\n",
        "print(maybeModule)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Qej_Z6V3mZnG"
      },
      "source": [
        "## `numpy.ndarray`로 변환\n",
        "\n",
        "다음 스위프트 자료형은 `numpy.ndarray`로 변환할 수 있습니다:\n",
        "- `Array<Element>`\n",
        "- `ShapedArray<Scalar>`\n",
        "- `Tensor<Scalar>`\n",
        "\n",
        "`Numpy.ndarray`의 `dtype`이 `Element` 또는 `Scalar`의 일반 파라미터 타입과 호환되어야만 변환이 성공합니다.\n",
        "\n",
        "`Array`의 경우 `numpy.ndarray`가 1-D일 경우에만 `numpy`에서 변환이 성공합니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "colab_type": "code",
        "id": "hPvKgZBeDQ1p",
        "outputId": "19f57e13-ec70-4156-cc83-687bfc1c185c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Swift type: PythonObject\r\n",
            "Python type: <class 'numpy.ndarray'>\r\n",
            "(4,)\r\n"
          ]
        }
      ],
      "source": [
        "import TensorFlow\n",
        "\n",
        "let numpyArray = np.ones([4], dtype: np.float32)\n",
        "print(\"Swift type:\", type(of: numpyArray))\n",
        "print(\"Python type:\", Python.type(numpyArray))\n",
        "print(numpyArray.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "colab_type": "code",
        "id": "ZuDgZ5cBS3Uk",
        "outputId": "bcc901e2-8fe8-47ba-ed15-5e4f3db25c42"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1. 1. 1. 1.]\r\n",
            "[1. 1. 1. 1.]\r\n",
            "[1. 1. 1. 1.]\r\n"
          ]
        }
      ],
      "source": [
        "// `numpy.ndarray`에서 스위프트 타입으로 변환하는 예제.\n",
        "let array: [Float] = Array(numpy: numpyArray)!\n",
        "let shapedArray = ShapedArray<Float>(numpy: numpyArray)!\n",
        "let tensor = Tensor<Float>(numpy: numpyArray)!\n",
        "\n",
        "// 스위프트 타입에서 `numpy.ndarray`으로 변환하는 예제.\n",
        "print(array.makeNumpyArray())\n",
        "print(shapedArray.makeNumpyArray())\n",
        "print(tensor.makeNumpyArray())\n",
        "\n",
        "// dtype이 다른 예제.\n",
        "let doubleArray: [Double] = Array(numpy: np.ones([3], dtype: np.float))!\n",
        "let intTensor = Tensor<Int32>(numpy: np.ones([2, 3], dtype: np.int32))!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "8EQFZZ5iafwh"
      },
      "source": [
        "## 이미지 표시\n",
        "\n",
        "파이썬 노트북에서처럼 `matplotlib`를 이용해 이미지를 결과 창에 표시할 수 있습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "colab_type": "code",
        "id": "vjQ7Rd3_IXuX",
        "outputId": "76e589e3-b9af-45d5-b849-e802d7b05952"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('inline', 'module://ipykernel.pylab.backend_inline')\n"
            ]
          },
          "execution_count": 13,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "// 주피터 노트북에 그래프를 표시하기 위한 셀입니다.\n",
        "// 다른 환경에서는 사용하지 마세요.\n",
        "%include \"EnableIPythonDisplay.swift\"\n",
        "IPythonDisplay.shell.enable_matplotlib(\"inline\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        },
        "colab_type": "code",
        "id": "jUzsa2cxafQV",
        "outputId": "b9bee13d-b727-4507-aa75-04d82b47aba2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "None\n"
            ]
          },
          "execution_count": 14,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "let np = Python.import(\"numpy\")\n",
        "let plt = Python.import(\"matplotlib.pyplot\")\n",
        "\n",
        "let time = np.arange(0, 10, 0.01)\n",
        "let amplitude = np.exp(-0.1 * time)\n",
        "let position = amplitude * np.sin(3 * time)\n",
        "\n",
        "plt.figure(figsize: [15, 10])\n",
        "\n",
        "plt.plot(time, position)\n",
        "plt.plot(time, amplitude)\n",
        "plt.plot(time, -amplitude)\n",
        "\n",
        "plt.xlabel(\"Time (s)\")\n",
        "plt.ylabel(\"Position (m)\")\n",
        "plt.title(\"Oscillations\")\n",
        "\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "python_interoperability.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Swift",
      "name": "swift"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
